TCGA analysis
Florian Uhlitz
2021-03-22
library(survminer)
library(survival)
library(magrittr)
library(tidyverse)
library(Seurat)
library(readxl)
library(cowplot)
library(colorblindr)
library(viridis)
library(consensusOV)
library(progeny)
source("_src/global_vars.R")1 Idea
- Observation from SPECTRUM: Endothelial and Fibroblast sub cell types have different compositions across patients, tumor sites and treatment status
- TCGA transcriptional sub types reflect cell type composition
- Can we use cell type markers defined in SPECTRUM to deconvolute cell type composition in bulk RNA-seq data, and based on this, relate PFS, OS, or treatment response to cell type composition?
2 TCGA data overview
## load data ------------------------------------------------------
file_paths <- list.files("/work/shah/uhlitzf/data/TCGA/OV/htseq", full.names = T)
## sample sheet
sample_tbl <- tibble(`File Name` = paste0(basename(file_paths), ".gz")) %>%
left_join(read_tsv("/work/shah/uhlitzf/data/TCGA/OV/gdc_sample_sheet.2020-07-28.tsv"), by = "File Name") %>%
set_colnames(str_replace_all(tolower(colnames(.)), " ", "_")) %>%
select(case_id, sample_id, file_name)
sample_df <- sample_tbl %>%
as.data.frame %>%
set_rownames(.$sample_id)
# ## raw counts ---------------------------
#
# read_c2 <- function(x) select(read_tsv(x, col_names = F), -1)
# count_tbl <- lapply(file_paths, read_c2) %>%
# bind_cols() %>%
# set_colnames(sample_tbl$sample_id) %>%
# mutate(ENSEMBL = read_tsv(file_paths[1], col_names = F)$X1) %>%
# select(ENSEMBL, everything())
# write_tsv(count_tbl, "/work/shah/uhlitzf/data/TCGA/OV/all_counts.tsv")
#
# count_tbl <- read_tsv("/work/shah/uhlitzf/data/TCGA/OV/all_counts.tsv")
# count_mat <- as.matrix(select(count_tbl, -1)) %>%
# set_rownames(count_tbl$ENSEMBL)
#
# ## normalization ------------------------
#
# dds <- DESeq2::DESeqDataSetFromMatrix(countData = count_mat, colData = sample_df, design = ~sample_id)
#
# dds <- DESeq2::estimateSizeFactors(dds)
# write_rds(dds, "/work/shah/uhlitzf/data/TCGA/OV/deseq_obj.rds")
dds <- read_rds("/work/shah/uhlitzf/data/TCGA/OV/deseq_obj.rds")
count_norm <- DESeq2::counts(dds, normalized = T)
## annotation ------------------------------
rownames(count_norm) <- str_split(rownames(count_norm), "\\.") %>%
lapply(function(x) x[1]) %>%
unlist
gene_symbol <- AnnotationDbi::select(org.Hs.eg.db::org.Hs.eg.db,
keys = rownames(count_norm),
columns = c("ENSEMBL", "SYMBOL", "UNIPROT"),
keytype = "ENSEMBL") %>%
distinct(SYMBOL, .keep_all = T) %>%
distinct(ENSEMBL, .keep_all = T) %>%
na.omit() %>%
select(-UNIPROT) %>%
deframe
count_norm_symbol <- count_norm[names(gene_symbol),]
rownames(count_norm_symbol) <- gene_symbol
## embeddings ----------------------------------
tcga_pca <- prcomp(t(count_norm_symbol))
set.seed(123)
tcga_umap <- uwot::umap(tcga_pca$x[,1:50]) %>%
set_colnames(c("UMAP_1", "UMAP_2")) %>%
as_tibble() %>%
mutate(sample_id = rownames(tcga_pca$x))
# ## consensusOV --------------------------------
# gene_entrez <- AnnotationDbi::select(org.Hs.eg.db::org.Hs.eg.db,
# keys = rownames(count_norm),
# columns = c("ENSEMBL", "ENTREZID"),
# keytype = "ENSEMBL") %>%
# distinct(ENTREZID, .keep_all = T) %>%
# distinct(ENSEMBL, .keep_all = T) %>%
# deframe
#
# count_norm_entrez <- count_norm[names(gene_entrez),]
# rownames(count_norm_entrez) <- gene_entrez
#
# covs <- get.subtypes(count_norm_entrez, rownames(count_norm_entrez), method = "consensusOV")
# covs_tbl <- cbind(as.data.frame(covs[[2]]), covs[[1]]) %>%
# as_tibble(rownames = "sample_id") %>%
# setNames(c(names(.)[-6], "consensusOV"))
#
# consensusOV_names <- c(IMR_consensus = "Immunoreactive", MES_consensus = "Mesenchymal", PRO_consensus = "Proliferative", DIF_consensus = "Differentiated")
# covs_tbl <- covs_tbl %>%
# mutate(consensusOV = as.character(consensusOV)) %>%
# mutate(consensusOV = consensusOV_names[consensusOV])
# write_tsv(covs_tbl, "/work/shah/uhlitzf/data/TCGA/OV/tcga_consensusOV.tsv")
covs_tbl <- read_tsv("/work/shah/uhlitzf/data/TCGA/OV/tcga_consensusOV.tsv")
# ## progeny --------------------------------
gene_hugo <- AnnotationDbi::select(org.Hs.eg.db::org.Hs.eg.db,
keys = rownames(count_norm),
columns = c("ENSEMBL", "SYMBOL"),
keytype = "ENSEMBL") %>%
distinct(SYMBOL, .keep_all = T) %>%
distinct(ENSEMBL, .keep_all = T) %>%
deframe
count_norm_hugo <- count_norm[names(gene_hugo),]
rownames(count_norm_hugo) <- gene_hugo
## compute progeny scores
progeny_tbl <- count_norm_hugo %>%
progeny %>%
as.data.frame %>%
as_tibble() %>%
setNames(paste0(make.names(names(.)), ".pathway")) %>%
mutate(sample_id = colnames(count_norm_hugo)) %>%
select(sample_id, everything())
## clinical data ----------------------------------
clin_tbl <- read_tsv("/work/shah/uhlitzf/data/TCGA/OV/ov_tcga_pan_can_atlas_2018_clinical_data.tsv") %>%
set_colnames(str_replace_all(tolower(colnames(.)), " ", "_")) %>%
select(case_id = patient_id,
age = diagnosis_age,
final_pfstm = `progress_free_survival_(months)`,
final_pfsid = progression_free_status,
final_ostm = `overall_survival_(months)`,
final_osid = overall_survival_status) %>%
distinct(case_id, .keep_all = T) %>%
mutate(final_pfsid = as.numeric(str_remove_all(final_pfsid, ":PROGRESSION|:CENSORED")),
final_osid = as.numeric(str_remove_all(final_osid, ":DECEASED|:LIVING")))
mut_tbl <- read_tsv("/work/shah/uhlitzf/data/TCGA/OV/TCGA-OV-BRCA-status.tsv") %>%
select(case_id = `Patient ID`, BRCA1, BRCA2) %>%
mutate(brca_group = case_when(
BRCA1 == "no alteration" & BRCA2 == "no alteration" ~ "WT",
BRCA1 != "no alteration" & BRCA2 != "no alteration" ~ "double mut",
BRCA1 != "no alteration" ~ "BRCA1",
BRCA2 != "no alteration" ~ "BRCA2"
))
## join data for plotting ------------------------
plot_data <- tcga_umap %>%
left_join(select(sample_tbl, -file_name), by = "sample_id") %>%
left_join(select(covs_tbl, sample_id, consensusOV), by = "sample_id") %>%
left_join(clin_tbl, by = "case_id") %>%
left_join(mut_tbl, by = "case_id") %>%
left_join(progeny_tbl, by = "sample_id")2.1 Cohort size
The TCGA OV RNA-seq data set consists of bulk RNA-seq data from a cohort of 379 ovarian cancer patients.
plot_data %>%
group_by(consensusOV) %>%
tally %>%
ggplot() +
geom_bar(aes(consensusOV, n, fill = consensusOV), stat = "identity")+
labs(y = "# cases") +
scale_fill_manual(values = clrs$consensusOV)
plot_data %>%
group_by(brca_group) %>%
tally %>%
ggplot() +
geom_bar(aes(brca_group, n, fill = brca_group), stat = "identity")+
labs(y = "# cases") +
scale_fill_manual(values = clrs$brca_group)
2.2 Cohort features
2.2.1 UMAPs
Top 50 PCs were computed on all genes and used as input for UMAP. One dot represents one TCGA case.
p1 <- ggplot(plot_data) +
geom_point(aes(UMAP_1, UMAP_2, color = consensusOV)) +
NoAxes() +
scale_color_manual(values = clrs$consensusOV)
p2 <- ggplot(plot_data) +
geom_point(aes(UMAP_1, UMAP_2, color = age)) +
NoAxes() +
scale_color_viridis_c()
p3 <- ggplot(plot_data) +
geom_point(aes(UMAP_1, UMAP_2, color = as.logical(final_pfsid))) +
NoAxes()
p4 <- ggplot(plot_data) +
geom_point(aes(UMAP_1, UMAP_2, color = brca_group)) +
scale_color_manual(values = clrs$brca_group) +
NoAxes()
plot_grid(p1, p2, p3, p4, ncol = 2, align = "hv")
2.2.2 pathway expression
plot_data %>%
gather(pathway, value, -c(1:13)) %>%
ggplot() +
geom_boxplot(aes(brca_group, value, color = brca_group)) +
facet_wrap(~pathway, scales = "free") +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
scale_color_manual(values = clrs$brca_group)
2.3 Survival plots
PFS and OS are overall higher for patients of the immunoreactive subtype.
pfsfit <- survfit(Surv(final_pfstm/12, final_pfsid) ~ consensusOV, data = plot_data)
osfit <- survfit(Surv(final_ostm/12, final_osid) ~ consensusOV, data = plot_data)
ggsurvplot(pfsfit, data = plot_data,
palette = setNames(clrs$consensusOV,
paste0("consensusOV=", names(clrs$consensusOV)))) +
labs(x = "Time [years]",
y = "PFS")
ggsurvplot(osfit, data = plot_data,
palette = setNames(clrs$consensusOV,
paste0("consensusOV=", names(clrs$consensusOV)))) +
labs(x = "Time [years]",
y = "OS")
3 TCGA gene expression data
The gene expression data can be used to identify potential bio markers for better stratification.
expression_cutoff <- 2
## Cluster signatures from SPECTRUM
marker_tbl_fb <- read_tsv("/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/supplementary_tables/Fibroblast.super_marker_sheet.tsv")
marker_tbl_ml <- read_tsv("/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/supplementary_tables/Myeloid.super_marker_sheet.tsv")
marker_tbl_tc <- read_tsv("/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/supplementary_tables/T.super_marker_sheet_full.tsv")
marker_tbl_cc <- read_tsv("/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/supplementary_tables/Ovarian.cancer.super_marker_sheet.tsv")
cluster_label_names <- c(unlist(lapply(clrs$cluster_label, names), use.names = F), unlist(lapply(clrs$cluster_label_sub, names), use.names = F))
wrap_markers <- . %>%
.[,colnames(.)[colnames(.) %in% cluster_label_names]] %>%
slice(1:10) %>%
# select(-rank) %>%
gather(cluster, gene_id)
markers_tbl <- list(T.cell = wrap_markers(marker_tbl_tc),
Fibroblast = wrap_markers(marker_tbl_fb),
Myeloid.cell = wrap_markers(marker_tbl_ml),
Ovarian.cancer.cell = wrap_markers(marker_tbl_cc)) %>%
bind_rows(.id = "cell_type")
gois <- unique(markers_tbl$gene_id)
gois <- gois[gois %in% rownames(count_norm_symbol)]
## filter TCGA data for SPECTRUM markers
full_data <- count_norm_symbol[gois,] %>%
as.data.frame %>%
as_tibble(rownames = "gene_id") %>%
gather(sample_id, exprs, -gene_id) %>%
left_join(select(sample_tbl, -file_name), by = "sample_id") %>%
left_join(select(covs_tbl, sample_id, consensusOV), by = "sample_id") %>%
left_join(clin_tbl, by = "case_id") %>%
left_join(mut_tbl, by = "case_id") %>%
left_join(markers_tbl, by = "gene_id") %>%
mutate(log_exprs = log2(exprs+1)) %>%
group_by(gene_id) %>%
mutate(mean_exprs = mean(log_exprs, na.rm = T),
sd_exprs = sd(log_exprs, na.rm = T),
rel_exprs = (log_exprs-mean_exprs)/sd_exprs,
cut_exprs = ifelse(rel_exprs < -expression_cutoff, -expression_cutoff, ifelse(rel_exprs > expression_cutoff, expression_cutoff, rel_exprs)))3.1 SPECTRUM cell type markers in TCGA cohort
Top cluster markers differ in expression with respect to TCGA transcriptional sub type status.
3.1.1 heatmaps
Heatmaps show markers in columns and patients in rows.
heatmap_layers <- list(
geom_tile(),
facet_grid(consensusOV~cluster, scales = "free", space = "free"),
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
axis.text.y = element_blank(),
axis.ticks = element_blank()),
scale_fill_gradient2(low = "steelblue", high = "red")
)
ggplot(filter(full_data, cell_type == "T.cell"),
aes(gene_id, sample_id, fill = cut_exprs)) +
heatmap_layers
ggplot(filter(full_data, cell_type == "Fibroblast"),
aes(gene_id, sample_id, fill = cut_exprs)) +
heatmap_layers
ggplot(filter(full_data, cell_type == "Myeloid.cell"),
aes(gene_id, sample_id, fill = cut_exprs)) +
heatmap_layers
ggplot(filter(full_data, cell_type == "Ovarian.cancer.cell"),
aes(gene_id, sample_id, fill = cut_exprs)) +
heatmap_layers
heatmap_layers <- list(
geom_tile(),
facet_grid(brca_group~cluster, scales = "free", space = "free"),
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
axis.text.y = element_blank(),
axis.ticks = element_blank()),
scale_fill_gradient2(low = "steelblue", high = "red")
)
ggplot(filter(full_data, cell_type == "T.cell"),
aes(gene_id, sample_id, fill = cut_exprs)) +
heatmap_layers
ggplot(filter(full_data, cell_type == "Fibroblast"),
aes(gene_id, sample_id, fill = cut_exprs)) +
heatmap_layers
ggplot(filter(full_data, cell_type == "Myeloid.cell"),
aes(gene_id, sample_id, fill = cut_exprs)) +
heatmap_layers
ggplot(filter(full_data, cell_type == "Ovarian.cancer.cell"),
aes(gene_id, sample_id, fill = cut_exprs)) +
heatmap_layers
### boxplots
cluster_summary <- full_data %>%
group_by(cluster, sample_id, consensusOV, cell_type) %>%
summarise(cluster_score = median(rel_exprs, na.rm = T))
# plot_cOV <- function(cOV) {
#
# cluster_summary %>%
# filter(consensusOV == cOV) %>%
# group_by(cluster) %>%
# mutate(median = median(cluster_score, na.rm = T)) %>%
# ungroup() %>%
# arrange(median) %>%
# mutate(cluster = ordered(cluster, levels = unique(cluster))) %>%
# ggplot(aes(cluster, cluster_score, color = cell_type)) +
# geom_boxplot() +
# geom_hline(yintercept = 0, linetype = 2) +
# scale_color_manual(values = clrs$cell_type) +
# theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
# labs(title = cOV, x = "", y = "Marker gene score")
#
# }
#
# plot_grid(plot_cOV("Immunoreactive"),
# plot_cOV("Mesenchymal"),
# plot_cOV("Proliferative"),
# plot_cOV("Differentiated"), ncol = 2, align = "hv")3.1.2 score distributions
main_clusters_tbl <- cluster_summary %>%
filter(cluster %in% c("M2.CXCL10", "Activated.CAF.TGFb")) %>%
select(-cell_type) %>%
spread(cluster, cluster_score) %>%
mutate(diff_score = Activated.CAF.TGFb - M2.CXCL10) %>%
gather(cluster, cluster_score, -c(1:2))
ggplot(main_clusters_tbl) +
geom_density(aes(cluster_score, color = consensusOV)) +
scale_color_manual(values = clrs$consensusOV) +
facet_wrap(~cluster)
4 Marker expression-based patient strata
- low: 1st expression quartile
- high: 4th expression quartile
strata_tbl <- full_data %>%
distinct(gene_id, sample_id, .keep_all = T) %>%
group_by(gene_id) %>%
mutate(q25 = quantile(cut_exprs, probs = 0.25, na.rm = T),
q75 = quantile(cut_exprs, probs = 0.75, na.rm = T),
strata = ifelse(cut_exprs < q25, "low", ifelse(cut_exprs > q75, "high", "mid"))) %>%
ungroup
survplot_gene <- function(gene) {
data_tbl <- filter(strata_tbl, gene_id == gene, strata != "mid")
data_tbl$final_pfstm <- data_tbl$final_pfstm/12
fit <- survfit(Surv(final_pfstm, final_pfsid) ~ strata, data = data_tbl)
ggsurvplot(fit, data = data_tbl, palette = c("red", "steelblue")) +
labs(x = "Time [years]",
y = "PFS",
title = gene)
}4.1 M2.CXCL10 marker
marker_c <- marker_tbl_ml$M2.CXCL10[marker_tbl_ml$M2.CXCL10 %in% full_data$gene_id][1:10] %>% na.omit()
for(i in 1:length(marker_c)){
cat('###', marker_c[i],' \n')
print(survplot_gene(marker_c[i]))
cat(' \n \n')
}4.1.1 CXCL10
4.1.2 CXCL11
4.1.3 CCL8
4.1.4 IFIT2
4.1.5 IFIT1
4.1.6 TNFSF10
4.1.7 ISG15
4.1.8 RSAD2
4.1.9 IFIT3
4.1.10 GBP1
4.2 M2.MARCO marker
marker_c <- marker_tbl_ml$M2.MARCO[marker_tbl_ml$M2.MARCO %in% full_data$gene_id][1:10] %>% na.omit()
for(i in 1:length(marker_c)){
cat('###', marker_c[i],' \n')
print(survplot_gene(marker_c[i]))
cat(' \n \n')
}4.2.1 MT1G
4.2.2 MARCO
4.2.3 MT1X
4.2.4 MT1H
4.2.5 PLIN2
4.2.6 CXCL3
4.2.7 CCL20
4.2.8 FN1
4.2.9 TIMP1
4.2.10 MT2A
4.3 TGFb.CAF marker
marker_c <- marker_tbl_fb$Activated.CAF.TGFb[marker_tbl_fb$Activated.CAF.TGFb %in% full_data$gene_id][1:10] %>% na.omit()
for(i in 1:length(marker_c)){
cat('###', marker_c[i],' \n')
print(survplot_gene(marker_c[i]))
cat(' \n \n')
}4.3.1 MMP11
4.3.2 CTHRC1
4.3.3 COL11A1
4.3.4 POSTN
4.3.5 CTSK
4.3.6 COL1A2
4.3.7 COL12A1
4.3.8 VCAN
4.3.9 COL1A1
4.3.10 COL6A3
5 session info
devtools::session_info()## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
## version R version 3.6.2 (2019-12-12)
## os Debian GNU/Linux 10 (buster)
## system x86_64, linux-gnu
## ui X11
## language (EN)
## collate en_US.UTF-8
## ctype en_US.UTF-8
## tz Etc/UTC
## date 2021-03-22
##
## ─ Packages ───────────────────────────────────────────────────────────────────
## package * version date lib
## abind 1.4-5 2016-07-21 [2]
## acepack 1.4.1 2016-10-29 [2]
## annotate 1.64.0 2019-10-29 [2]
## AnnotationDbi 1.48.0 2019-10-29 [2]
## ape 5.3 2019-03-17 [2]
## assertthat 0.2.1 2019-03-21 [2]
## backports 1.1.10 2020-09-15 [1]
## base64enc 0.1-3 2015-07-28 [2]
## bibtex 0.4.2.2 2020-01-02 [2]
## Biobase 2.46.0 2019-10-29 [2]
## BiocGenerics 0.32.0 2019-10-29 [2]
## BiocParallel 1.20.1 2019-12-21 [2]
## bit 1.1-15.1 2020-01-14 [2]
## bit64 0.9-7 2017-05-08 [2]
## bitops 1.0-6 2013-08-17 [2]
## blob 1.2.0 2019-07-09 [2]
## broom 0.7.2 2020-10-20 [1]
## callr 3.4.2 2020-02-12 [1]
## car 3.0-8 2020-05-21 [1]
## carData 3.0-4 2020-05-22 [1]
## caTools 1.17.1.4 2020-01-13 [2]
## cellranger 1.1.0 2016-07-27 [2]
## checkmate 2.0.0 2020-02-06 [1]
## cli 2.0.2 2020-02-28 [1]
## cluster 2.1.0 2019-06-19 [3]
## codetools 0.2-16 2018-12-24 [3]
## colorblindr * 0.1.0 2020-01-13 [2]
## colorspace * 1.4-2 2019-12-29 [2]
## consensusOV * 1.8.1 2019-12-05 [1]
## cowplot * 1.0.0 2019-07-11 [2]
## crayon 1.3.4 2017-09-16 [1]
## curl 4.3 2019-12-02 [2]
## data.table 1.12.8 2019-12-09 [2]
## DBI 1.1.0 2019-12-15 [2]
## dbplyr 2.0.0 2020-11-03 [1]
## DelayedArray 0.12.2 2020-01-06 [2]
## desc 1.2.0 2018-05-01 [2]
## DESeq2 1.26.0 2019-10-29 [2]
## devtools 2.2.1 2019-09-24 [2]
## digest 0.6.25 2020-02-23 [1]
## dplyr * 1.0.2 2020-08-18 [1]
## ellipsis 0.3.1 2020-05-15 [1]
## evaluate 0.14 2019-05-28 [2]
## fansi 0.4.1 2020-01-08 [2]
## farver 2.0.3 2020-01-16 [1]
## fastmap 1.0.1 2019-10-08 [2]
## fitdistrplus 1.0-14 2019-01-23 [2]
## FNN 1.1.3 2019-02-15 [2]
## forcats * 0.5.0 2020-03-01 [1]
## foreign 0.8-74 2019-12-26 [3]
## Formula 1.2-3 2018-05-03 [2]
## fs 1.5.0 2020-07-31 [1]
## future 1.15.1 2019-11-25 [2]
## future.apply 1.4.0 2020-01-07 [2]
## gbRd 0.4-11 2012-10-01 [2]
## gdata 2.18.0 2017-06-06 [2]
## genefilter 1.68.0 2019-10-29 [2]
## geneplotter 1.64.0 2019-10-29 [2]
## generics 0.0.2 2018-11-29 [2]
## GenomeInfoDb 1.22.0 2019-10-29 [2]
## GenomeInfoDbData 1.2.2 2020-01-14 [2]
## GenomicRanges 1.38.0 2019-10-29 [2]
## ggplot2 * 3.3.2 2020-06-19 [1]
## ggpubr * 0.4.0 2020-06-27 [1]
## ggrepel 0.8.1 2019-05-07 [2]
## ggridges 0.5.2 2020-01-12 [2]
## ggsignif 0.6.0 2019-08-08 [1]
## globals 0.12.5 2019-12-07 [2]
## glue 1.3.2 2020-03-12 [1]
## gplots 3.0.1.2 2020-01-11 [2]
## graph 1.64.0 2019-10-29 [1]
## gridExtra 2.3 2017-09-09 [2]
## GSEABase 1.48.0 2019-10-29 [1]
## GSVA 1.34.0 2019-10-29 [1]
## gtable 0.3.0 2019-03-25 [2]
## gtools 3.8.1 2018-06-26 [2]
## haven 2.3.1 2020-06-01 [1]
## Hmisc 4.3-0 2019-11-07 [2]
## hms 0.5.3 2020-01-08 [1]
## htmlTable 1.13.3 2019-12-04 [2]
## htmltools 0.5.1.1 2021-01-22 [1]
## htmlwidgets 1.5.1 2019-10-08 [2]
## httpuv 1.5.2 2019-09-11 [2]
## httr 1.4.2 2020-07-20 [1]
## ica 1.0-2 2018-05-24 [2]
## igraph 1.2.5 2020-03-19 [1]
## IRanges 2.20.2 2020-01-13 [2]
## irlba 2.3.3 2019-02-05 [2]
## jpeg 0.1-8.1 2019-10-24 [2]
## jsonlite 1.7.1 2020-09-07 [1]
## KernSmooth 2.23-16 2019-10-15 [3]
## km.ci 0.5-2 2009-08-30 [1]
## KMsurv 0.1-5 2012-12-03 [1]
## knitr 1.26 2019-11-12 [2]
## labeling 0.3 2014-08-23 [2]
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